A blind method for SIMO channel identification and equalization

Abstract

Subspace (SS) methods are effective approaches for blind channel identification, for they achieve a good performance with relatively short data lengths and work well at low signal to noise ratio (SNR). However, they require the accurate knowledge of the channel order, which is difficult in a noisy environment. Although linear prediction (LP) based methods can handle the problem of channel order overestimation, they are sensitive to observation noise. In this paper, we proposed a blind channel identification and equalization algorithm, based on the eigen analysis of shifted correlation matrices and their associated properties. Although the algorithm's performance is not the optimal in some cases, it is robust to channel order overestimation and not sensitive to observation noise as well. Furthermore, the algorithm does not require the computation of the correlation matrix pseudo-inverse, as with linear prediction algorithms, nor are the whole noise or signal eigen vectors necessary to achieve identification as with the subspace algorithm. So it is computationally efficient. Theoretical analysis and simulation results are provided to verify these facts.